Research Article
Global Convergence of a Modified Two-Parameter Scaled BFGS Method with Yuan-Wei-Lu Line Search for Unconstrained Optimization
Table 4
The numerical results for problems 35–51.
| MTPSBFGS-YWL | SBFGS-WWP | No. | Dim | NI | NFG | CPU time | NI | NFG | CPU time |
| 35 | 300 | 4 | 8 | 0 | 4 | 8 | 0 | 35 | 900 | 7 | 14 | 1.59375 | 7 | 14 | 1.59375 | 35 | 2700 | 11 | 22 | 46.875 | 11 | 22 | 46.40625 | 36 | 300 | 24 | 59 | 0.359375 | 26 | 64 | 0.46875 | 36 | 900 | 23 | 66 | 6.359375 | 20 | 58 | 5.59375 | 36 | 2700 | 18 | 49 | 82.359375 | 19 | 46 | 85.5625 | 37 | 300 | 136 | 275 | 1.65625 | 136 | 275 | 1.6875 | 37 | 900 | 235 | 473 | 67.40625 | 235 | 473 | 67.359375 | 37 | 2700 | 441 | 886 | 2240.015625 | 442 | 888 | 2234.09375 | 38 | 300 | 12 | 27 | 0.125 | 12 | 27 | 0.0625 | 38 | 900 | 12 | 26 | 2.8125 | 12 | 26 | 2.921875 | 38 | 2700 | 15 | 33 | 64.90625 | 15 | 33 | 64.65625 | 39 | 300 | 37 | 80 | 0.40625 | 37 | 80 | 0.46875 | 39 | 900 | 43 | 89 | 10.890625 | 43 | 91 | 11.265625 | 39 | 2700 | 26 | 52 | 116.703125 | 26 | 52 | 116.078125 | 40 | 300 | 532 | 1329 | 7.40625 | 958 | 1925 | 12.96875 | 40 | 900 | 546 | 1364 | 150.921875 | 1000 | 2008 | 274.5625 | 40 | 2700 | 644 | 1605 | 3124.65625 | 1000 | 2014 | 4810.296875 | 41 | 300 | 18 | 41 | 0.1875 | 20 | 45 | 0.1875 | 41 | 900 | 19 | 43 | 4.828125 | 19 | 43 | 4.734375 | 41 | 2700 | 19 | 43 | 85.0625 | 19 | 43 | 84.875 | 42 | 300 | 19 | 65 | 0.15625 | 16 | 57 | 0.078125 | 42 | 900 | 4 | 21 | 0.046875 | 4 | 21 | 0.046875 | 42 | 2700 | 4 | 21 | 0.578125 | 4 | 21 | 0.515625 | 43 | 300 | 22 | 48 | 0.265625 | 25 | 54 | 0.234375 | 43 | 900 | 23 | 50 | 5.84375 | 27 | 58 | 6.984375 | 43 | 2700 | 25 | 54 | 114.328125 | 29 | 62 | 133.78125 | 44 | 300 | 38 | 80 | 0.453125 | 39 | 82 | 0.546875 | 44 | 900 | 36 | 76 | 9.703125 | 43 | 90 | 11.5 | 44 | 2700 | 39 | 82 | 180.453125 | 46 | 96 | 212.125 | 45 | 300 | 17 | 40 | 0.171875 | 18 | 42 | 0.28125 | 45 | 900 | 17 | 40 | 4.265625 | 18 | 42 | 4.65625 | 45 | 2700 | 18 | 42 | 80.171875 | 19 | 44 | 85.09375 | 46 | 300 | 104 | 255 | 1.5625 | 60 | 126 | 0.875 | 46 | 900 | 141 | 354 | 41.203125 | 87 | 180 | 24.796875 | 46 | 2700 | 164 | 418 | 829.3125 | 116 | 238 | 586.296875 | 47 | 300 | 37 | 80 | 0.734375 | 36 | 78 | 0.71875 | 47 | 900 | 44 | 95 | 14.640625 | 44 | 95 | 14.78125 | 47 | 2700 | 13 | 31 | 64.21875 | 13 | 31 | 63.9375 | 48 | 300 | 26 | 52 | 0.296875 | 26 | 52 | 0.3125 | 48 | 900 | 48 | 96 | 12.890625 | 48 | 96 | 12.890625 | 48 | 2700 | 22 | 47 | 99.546875 | 22 | 47 | 99.96875 | 49 | 300 | 76 | 154 | 0.953125 | 76 | 154 | 0.859375 | 49 | 900 | 133 | 268 | 37.203125 | 134 | 270 | 37.59375 | 49 | 2700 | 232 | 466 | 1161.109375 | 234 | 470 | 1176.921875 | 50 | 300 | 76 | 154 | 1.234375 | 75 | 152 | 1.328125 | 50 | 900 | 132 | 266 | 38.859375 | 132 | 266 | 38.53125 | 50 | 2700 | 231 | 464 | 1165.1875 | 232 | 466 | 1170 | 51 | 300 | 23 | 48 | 0.296875 | 23 | 48 | 0.3125 | 51 | 900 | 23 | 48 | 5.890625 | 23 | 48 | 5.859375 | 51 | 2700 | 23 | 48 | 102.484375 | 23 | 48 | 103.453125 |
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